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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Explicit Weierstrass traveling wave solutions and bifurcation analysis for dissipative Zakharov–Kuznetsov modified equal width equation

We consider the dissipative Zakharov–Kuznetsov modified equal width (ZK-MEW) equation and discuss the effect of the dissipation on the existence and nature of traveling wave solutions of the equation. We use Lyapunov function and dynamical system theory in order to show that when viscosity term is added to the ZK-MEW equation, yet there exists still bounded traveling wave solutions in certain regions. Subsequently, we obtain general solution of the ZK-MEW equation in the presence and absence of viscosity in terms of Weirstrass ℘ functions and Jacobi elliptic functions. We also derive a new form of kink-type solution by exploring a factorization method based on functional transformation and the Abel’s first order nonlinear equation. Finally, we use the phase plane analysis and examine the stability of the viscous waves.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

On the connection between microbursts and nonlinear electronic structures in planetary radiation belts

Using a dynamical-system approach, we have investigated the efficiency of large-amplitude whistler waves for causing microburst precipitation in planetary radiation belts by modeling the microburst energy and particle fluxes produced as a result of nonlinear wave–particle interactions. We show that wave parameters, consistent with large-amplitude oblique whistlers, can commonly generate microbursts of electrons with hundreds of keV-energies as a result of Landau trapping. Relativistic microbursts (>1 MeV) can also be generated by a similar mechanism, but require waves with large propagation angles θ{sub kB}>50{sup ∘} and phase-speeds v{sub Φ}⩾c/9. Using our result for precipitating density and energy fluxes, we argue that holes in the distribution function of electrons near the magnetic mirror point can result in the generation of double layers and electron solitary holes consistent in scales (of the order of Debye lengths) to nonlinear structures observed in the radiation belts by the Van Allen Probes. Our results indicate a relationship between nonlinear electrostatic and electromagnetic structures in the dynamics of planetary radiation belts and their role in the cyclical production of energetic electrons (E⩾100 keV) on kinetic timescales, which is much faster than previously inferred.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2021

A model-independent data assimilation (MIDA) module and its applications in ecology

Models are an important tool to predict Earth system dynamics. An accurate prediction of future states of ecosystems depends on not only model structures but also parameterizations. Model parameters can be constrained by data assimilation. However, applications of data assimilation to ecology are restricted by highly technical requirements such as model-dependent coding. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module. MIDA works in three steps including data preparation, execution of data assimilation, and visualization. The first step prepares prior ranges of parameter values, a defined number of iterations, and directory paths to access files of observations and models. The execution step calibrates parameter values to best fit the observations and estimates the parameter posterior distributions. The final step automatically visualizes the calibration performance and posterior distributions. MIDA is model independent, and modelers can use MIDA for an accurate and efficient data assimilation in a simple and interactive way without modification of their original models. We applied MIDA to four types of ecological models: the data assimilation linked ecosystem carbon (DALEC) model, a surrogate-based energy exascale earth system model: the land component (ELM), nine phenological models and a stand-alone biome ecological strategy simulator (BiomeE). The applications indicate that MIDA can effectively solve data assimilation problems for different ecological models. Additionally, the easy implementation and model-independent feature of MIDA breaks the technical barrier of applications of data–model fusion in ecology. MIDA facilitates the assimilation of various observations into models for uncertainty reduction in ecological modeling and forecasting.

Earth system dynamics↗
NASA NTRS2003

Application of local Lyapunov exponents to maneuver design and navigation in the three-body problem

Dynamical systems theory has recently been employed to design trajectories within the three-body problem for several missions. This research has applied one stability technique, the calculation of local Lyapunov exponents, to such trajectories. Local Lyapunov exponents give an indication of the effects that perturbations or maneuvers will have on trajectories over a specified time. A numerical comparison of local Lyapunov exponents was first made with the distance random perturbations traveled from a nominal trajectory, and the local Lyapunov exponents were found to correspond well with the perturbations that caused the greatest deviation from the nominal. This would allow them to be used as an indicator of the points where it would be important to reduce navigation uncertainties.

dynamical systems theory↗
DOE OSTI2021

The Circular Economy Lifecycle Assessment and Visualization Framework: A Case Study of Wind Blade Circularity in Texas

Moving the current linear economy toward circularity is expected to have environmental, economic, and social impacts. Various modeling methods, including economic input-output modeling, life cycle assessment, agent-based modeling, and system dynamics, have been used to examine circular supply chains and analyze their impacts. This work describes the newly developed Circular Economy Lifecycle Assessment and Visualization (CELAVI) framework, which is designed to model how the impacts of supply chains might change as circularity increases. We first establish the framework with a discussion of modeling capabilities that are needed to capture circularity transitions; these capabilities are based on the fact that supply chains moving toward circularity are dynamic and therefore not at steady state, may encompass multiple industrial sectors or other interdependent supply chains and occupy a large spatial area. To demonstrate the capabilities of CELAVI, we present a case study on end-of-life wind turbine blades in the U.S. state of Texas. Our findings show that depending on exact process costs and transportation distances, mechanical recycling could lead to 69% or more of end-of-life turbine blade mass being kept in circulation rather than being landfilled, with only a 7.1% increase in global warming potential over the linear supply chain. We discuss next steps for framework development.

17 WIND ENERGY↗
DOE OSTI2021

Effectiveness and nonlinear characterization of vibro-impact energy harvesting absorbers in controlling base-excited systems

In this study, model derivations are carried out of a dynamical system under base excitations with a piezoelectric energy harvesting absorber as the tuned-mass-damper. Additionally, amplitude stoppers are included to the absorber in order to create a broadband resonant response, increasing the window of operational use for energy harvesting and system's control. This study is unique in the fact that the energy harvester is coupled to the source of its excitation. A nonlinear reduced-order model is developed using Euler–Lagrange principle and the Galerkin method to accurately estimate the energy harvesting absorber's displacement, harvested power, and the oscillating response of the primary structure. The nonlinear interaction of the energy harvesting absorber and the amplitude stoppers are the focus of this study, where an in-depth investigation of bifurcation points of the primary structure and energy harvesting absorber responses is performed. Due to a transfer of energy between the primary structure and the absorber, it is shown that a soft stopper with stiffness $5 \times {10^3}\,{\text{N}}\;{{\text{m}}^{ - 1}}\,$ has great control of the primary structure with 60% of the uncontrolled amplitude being reduced, as well as an increase of the harvested energy. Medium stoppers with small initial gaps size and hard stoppers do not control the primary structure and show a decrease in the energy harvesting capabilities due to the activation of the nonlinear contact-impact interactions. Finally, these stoppers also generate aperiodic regions due to the possible presence of grazing bifurcations.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Learning thermodynamic master equations for open quantum systems

The characterization of Hamiltonians and other components of open quantum dynamical systems plays a crucial role in quantum computing and other applications. Scientific machine learning techniques have been applied to this problem in a variety of ways, including by modeling with deep neural networks. However, the majority of mathematical models describing open quantum systems are linear, and the natural nonlinearities in learnable models have not been incorporated using physical principles. We present a data-driven model for open quantum systems that includes learnable, thermodynamically consistent terms. The trained model is interpretable, as it directly estimates the system Hamiltonian and linear components of coupling to the environment. We validate the model on synthetic two and three-level data, as well as experimental two-level data collected from a quantum device at Lawrence Livermore National Laboratory.

Mathematics and Computing↗
DOE OSTI2021

BETO 2021 Peer Review - WBS 4.1.2.32: Bioeconomy Scenario Analysis

The Bioeconomy Scenario Analysis project uses systems thinking and analysis to assess current and/or prospective techno-economics, research and development, deployment strategies, policy, and market conditions and their impact on the potential development trajectories of the bioenergy industry over time. Results from this project include identification of opportunities and constraints to industrial development, quantification of multiple metrics (energy, economic, environmental) and informing researchers, decision makers, and industry of the steps needed for a sustainable, nationwide biofuels industry. Analyses from this project enable the creation of a bioenergy industry by (1) inciting policy-makers to explore scenarios for nationwide biofuels production, identifying policy actions consistent with pathways for growth; (2) improving industry’s understanding of industry growth potential under different technology and investment conditions, better targeting their development efforts; and (3) providing universities and other interested stakeholders with tools and analyses that can be adapted to meet research and teaching objectives, connecting students with careers that build the industry. One of the many modeling tools used in this project, the Biomass Scenario Model (BSM) is a publicly-available, unique, validated, state-of-the-art, award-winning, fourth-generation model of the domestic biofuels supply chain which explicitly focuses on how and under what conditions biofuel technologies might be deployed to contribute to the U.S. transportation energy sector. We use models like the BSM to examine the implications of policies and incentives as well as their potential side-effects. The BSM uses a system-dynamics simulation to model dynamic interactions and transitions across the supply chain; it tracks the deployment of biofuels given industrial learning and the reaction of the investment community in the context of land availability, projected oil markets, consumer demand for biofuels, and government policies over time. Under expected market conditions, analyses using the BSM suggest that the biofuels industry may require significant external actions in the early years to thrive. Interventions that accelerate the industrial learning process (e.g. operation of pre-commercial and commercial facilities) have been identified as having strong influence in starting the growth of a commercial biofuel industry. Policies which are coordinated across the whole supply chain in BSM foster the growth of the biofuels industry and production of tens of billions of gallons of biofuels may occur under sufficiently favorable conditions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1988

Real-time flight test analysis and display techniques for the X-29A aircraft

The X-29A advanced technology demonstrator flight envelope expansion program and the subsequent flight research phase gave impetus to the development of several innovative real-time analysis and display techniques. These new techniques produced significant improvements in flight test productivity, flight research capabilities, and flight safety. These techniques include real-time measurement and display of in-flight structural loads, dynamic structural mode frequency and damping, flight control system dynamic stability and control response, aeroperformance drag polars, and aircraft specific excess power. Several of these analysis techniques also provided for direct comparisons of flight-measured results with analytical predictions. The aeroperformance technique was made possible by the concurrent development of a new simplified in-flight net thrust computation method. To achieve these levels of on-line flight test analysis, integration of ground and airborne systems was required. The capability of NASA Ames Research Center, Dryden Flight Research Facility's Western Aeronautical Test Range was a key factor in enabling implementation of these methods.

Hicks, John W.↗
NASA NTRS1989

Real-time flight test analysis and display techniques for the X-29A aircraft

The X-29A advanced technology demonstrator flight envelope expansion program and the subsequent flight research phase gave impetus to the development of several innovative real-time analysis and display techniques. These new techniques produced significant improvements in flight test productivity, flight research capabilities, and flight safety. These techniques include real-time measurement and display of in-flight structural loads, dynamic structural mode frequency and damping, flight control system dynamic stability and control response, aeroperformance drag polars, and aircraft specific excess power. Several of these analysis techniques also provided for direct comparisons of flight-measured results with analytical predictions. The aeroperformance technique was made possible by the concurrent development of a new simplified in-flight net thrust computation method. To achieve these levels of on-line flight test analysis, integration of ground and airborne systems was required. The capability of NASA Ames Research Center, Dryden Flight Research Facility's Western Aeronautical Test Range was a key factor to enable implementation of these methods.

Hicks, John W.↗
DOE OSTI2015

Biomass Scenario Model: BETO Analysis Platform Peer Review; NREL (National Renewable Energy Laboratory)

The Biomass Scenario Model (BSM) is a unique, carefully validated, state-of-the-art fourth-generation model of the domestic bioenergy supply chain which explicitly focuses on policy issues and their potential side effects. It integrates resource availability, behavior, policy, and physical, technological, and economic constraints. The BSM uses system-dynamics simulation to model dynamic interactions across the supply chain; it tracks the deployment of biofuels given technological development and the reaction of the investment community to those technologies in the context of land availability, the competing oil market, consumer demand for biofuels, and government policies over time. It places a strong emphasis on the behavior and decision-making of various economic agents. The model treats the major infrastructure-compatible fuels. Scenario analysis based on the BSM shows that the biofuels industry tends not to rapidly thrive without significant external actions in the early years of its evolution. An initial focus for jumpstarting the industry typically has strongest results in the BSM in areas where effects of intervention have been identified to be multiplicative. In general, we find that policies which are coordinated across the whole supply chain have significant impact in fostering the growth of the biofuels industry and that the production of tens of billions of gallons of biofuels may occur under sufficiently favorable conditions.

09 BIOMASS FUELS↗
DOE OSTI2021

Non-intrusive nonlinear model reduction via machine learning approximations to low-dimensional operators

Abstract Although projection-based reduced-order models (ROMs) for parameterized nonlinear dynamical systems have demonstrated exciting results across a range of applications, their broad adoption has been limited by their intrusivity: implementing such a reduced-order model typically requires significant modifications to the underlying simulation code. To address this, we propose a method that enables traditionally intrusive reduced-order models to be accurately approximated in a non-intrusive manner. Specifically, the approach approximates the low-dimensional operators associated with projection-based reduced-order models (ROMs) using modern machine-learning regression techniques. The only requirement of the simulation code is the ability to export the velocity given the state and parameters; this functionality is used to train the approximated low-dimensional operators. In addition to enabling nonintrusivity, we demonstrate that the approach also leads to very low computational complexity, achieving up to $$10^3{\times }$$ 10 3 × in run time. We demonstrate the effectiveness of the proposed technique on two types of PDEs. The domain of applications include both parabolic and hyperbolic PDEs, regardless of the dimension of full-order models (FOMs).

42 ENGINEERING↗
DOE OSTI2019

Reduced and Earlier Snowmelt Runoff Impacts Traditional Irrigation Systems

We show seasonal runoff from montane uplands is crucial for plant growth in agricultural communities of northern New Mexico. These communities typically employ traditional irrigation systems, called acequias, which rely mainly upon spring snowmelt runoff for irrigation. The trend of the past few decades is an increase in temperature, reduced snow pack, and earlier runoff from snowmelt across much of the western United States. In order to predict the potential impacts of changes in future climate a system dynamics model was constructed to simulate the surface water supplies in a montane upland watershed of a small irrigated community in northern New Mexico through the rest of the 21 st century. End-term simulations of representative concentration pathways (RCP) 4.5 and 8.5 suggest that runoff during the months of April to August could be reduced by 22% and 56%, respectively. End-term simulations also displayed a shift in the beginning and peak of snowmelt runoff by up to one month earlier than current conditions. Results suggest that rising temperatures will drive reduced runoff in irrigation season and earlier snowmelt runoff in the dry season towards the end of the 21 st century. Modeled results suggest that climate change leads to runoff scheme shift and increased frequency of drought; due to the uncontemporaneous of irrigation season and runoff scheme, water shortage will increase. Potential impacts of climate change scenarios and mitigation strategies should be further investigated to ensure the resilience of traditional agricultural communities in New Mexico and similar regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Deep decarbonization and U.S. biofuels production: a coordinated analysis with a detailed structural model and an integrated multisectoral model

Scenarios for deep decarbonization involve biomass for biofuels, biopower, and bioproducts, and they often include negative emissions via carbon capture and storage or utilization. However, critical questions remain about the feasibility of rapid growth to high levels of biomass utilization, given biomass and land availability as well as historical growth rates of the biofuel industry. We address these questions through a unique coordinated analysis and comparison of carbon pricing effects on biomass utilization growth in the United States using a multisectoral integrated assessment model, the Global Change Analysis Model (GCAM), and a biomass-to-biofuels system dynamics model, the Bioenergy Scenario Model (BSM). We harmonized and varied key factors—such as carbon prices, vehicle electrification, and arable land availability—in the two models. We varied the rate of biorefinery construction, the fungibility of feedstock types across conversion processes, and policy incentives in BSM. The rate of growth in biomass deployment under a carbon price in both models is within the range of current literature. However, the reallocation of land to biomass feedstocks would need to overcome bottlenecks to achieve growth consistent with deep decarbonization scenarios. Investments as a result of near-term policy incentives can develop technology and expand capacity—reducing costs, enabling flexibility in feedstock use, and improving stability—but if biomass demand is high, these investments might not overcome land reallocation bottlenecks. Biomass utilization for deep decarbonization relies on extraordinary growth in biomass availability and industrial capacity. In this paper, we quantify and describe the potential challenges of this rapid change.

09 BIOMASS FUELS↗
DOE OSTI2022

Tracing and Forecasting Metabolic Indices of Cancer Patients Using Patient-Specific Deep Learning Models

We develop a patient-specific dynamical system model from the time series data of the cancer patient’s metabolic panel taken during the period of cancer treatment and recovery. The model consists of a pair of stacked long short-term memory (LSTM) recurrent neural networks and a fully connected neural network in each unit. It is intended to be used by physicians to trace back and look forward at the patient’s metabolic indices, to identify potential adverse events, and to make short-term predictions. When the model is used in making short-term predictions, the relative error in every index is less than 10% in the L ∞ norm and less than 6.3% in the L 1 norm in the validation process. Once a master model is built, the patient-specific model can be calibrated through transfer learning. As an example, we obtain patient-specific models for four more cancer patients through transfer learning, which all exhibit reduced training time and a comparable level of accuracy. This study demonstrates that this modeling approach is reliable and can deliver clinically acceptable physiological models for tracking and forecasting patients’ metabolic indices.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1993

A fuzzy logic controller for an autonomous mobile robot

The ability of a mobile robot system to plan and move intelligently in a dynamic system is needed if robots are to be useful in areas other than controlled environments. An example of a use for this system is to control an autonomous mobile robot in a space station, or other isolated area where it is hard or impossible for human life to exist for long periods of time (e.g., Mars). The system would allow the robot to be programmed to carry out the duties normally accomplished by a human being. Some of the duties that could be accomplished include operating instruments, transporting objects, and maintenance of the environment. The main focus of our early work has been on developing a fuzzy controller that takes a path and adapts it to a given environment. The robot only uses information gathered from the sensors, but retains the ability to avoid dynamically placed obstacles near and along the path. Our fuzzy logic controller is based on the following algorithm: (1) determine the desired direction of travel; (2) determine the allowed direction of travel; and (3) combine the desired and allowed directions in order to determine a direciton that is both desired and allowed. The desired direction of travel is determined by projecting ahead to a point along the path that is closer to the goal. This gives a local direction of travel for the robot and helps to avoid obstacles.

Yen, John↗